A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

Rui Yu,Dejun Han,Baofeng Su,Xiaofeng Cao,Yulin Liu

doi:10.3390/rs13245173

Abstract

High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.

Highlights

SG_NDREI, SG_CIRE, SG_ARI2, SG_NGRDI, SG_GLI interval overlap were less than SG_NDRE, SG_NDVI, SG_NormR, etc between S1 and S2. These results suggested that stay green rates (SGR) indices may be alternative tools to reveal the dynamic changes of stay green in wheat populations
We used Multiple Linear Regression (MLR) and linear Support Vector Regression (SVR) to compare indices’ temporal SGR correlations with sample yields, considering that the dynamics of wheat stay green were mainly concentrated in the yield formation stage
According to the guidances of [3] and [37], we visually identified the stay green grade of the samples by visually scoring whole plant senescence and the portion of green leaf area in the flag leaf at the middle and later grain filling stages

Summary

Introduction

Wheat yield mainly comes from canopy organ (leaf and spike) photosynthesis during the grain filling stage. For wheat SG, precision screening and identification are difficult to achieve by traditional phenotyping methods (visual scoring, physiological and biochemical trait measurement, etc.) with great subjective influence and disadvantages of being time consuming and laborious in natural populations in the field. In this case, a large number of diversified genotypes are usually planted in hundreds of plots. Vegetation indices (spectral indices) or color indices derived from multispectral or RGB imagery are widely used in crop diversified phenotyping, such as pigments (chlorophyll, carotenoids, anthocyanins) content detection [20], phenology determination [21,22,23,24], canopy greenness, or vigor assessment [25,26], yield prediction [27,28,29], and stress monitoring [30,31]

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